Inspired by this strategy, we propose “SNR improvement (SNRi) target training”. The SE frontend uses an auxiliary scalar input, which represents the target SNRi of the output signal. Instead of optimizing the enhancement to maximize the SNR, its goal is to produce an output signal with the specified target SNRi. This can be easily incorporated into the time-domain SE frame-work and enables end-to-end training of a purely neural-network-based SE-ASR joint model. In the joint training stage, we additionally use the SNRi prediction network, which predicts a target SNRi value to minimize the ASR loss. Experiments show that (i) SNRi target training enables control of the SNRi of the output signal, and (ii) the joint training relatively reduces word-error-rate (WER) by 12% compared to a large-scale Conformer-transducer-based ASR system with 118.8M parameters [1]}.
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